Development of a DNA computing processor for high-precision breast cancer detection via miRNA biomarker analysis

Early and accurate diagnosis of breast cancer, due to the complexity and cost of conventional methods, remains a major challenge. A new DNA computing-based detection processor is proposed for high-precision breast cancer diagnosis through miRNA biomarker analysis. RNA sequencing data of 1103 cancer and 104 standard samples from The Cancer Genome Atlas (TCGA) are analyzed through Differential Expression Gene (DEG) analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify the critical up-regulated and down-regulated miRNAs. The selected miRNAs are integrated into a logical processor framework by applying DNA strand displacement and logic gate design. The processor consists of an increasing detector for oncogenic miRNAs and a decreasing detector for tumor-suppressor miRNAs components. Simulation results indicate a robust performance, with positive and negative predictive values of 0.91 and 0.98, respectively. Experimental validation confirms the functionality of miRNAs (miR-200a and miR-141) representatives, which supports the feasibility of this proposed design. A novel, enzyme-free, scalable, and cost-effective DNA computing system for enhancing biomarker-based breast cancer detection is proposed here.